diff options
| author | ziejd2 | 2017-09-28 15:04:40 -0500 |
|---|---|---|
| committer | ziejd2 | 2017-09-28 15:04:40 -0500 |
| commit | 8070dc963753142bb86c4ed698d91fd623ed28e7 (patch) | |
| tree | d0f6dd8fc46a49b819aa55c1a90faa14d8448883 /sourcecodes/bnt-master/nethelp3.3/mlpbkp.htm | |
| parent | 7cc31810d53176e805532b2789955f4eedbce6bb (diff) | |
| download | BNW-8070dc963753142bb86c4ed698d91fd623ed28e7.tar.gz | |
BNW using Octave instead of Matlab.
This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning. I am calling this BNW_1.02. It can be accessed at: compbio.uthsc.edu/BNW_1.02
Diffstat (limited to 'sourcecodes/bnt-master/nethelp3.3/mlpbkp.htm')
| -rw-r--r-- | sourcecodes/bnt-master/nethelp3.3/mlpbkp.htm | 50 |
1 files changed, 50 insertions, 0 deletions
diff --git a/sourcecodes/bnt-master/nethelp3.3/mlpbkp.htm b/sourcecodes/bnt-master/nethelp3.3/mlpbkp.htm new file mode 100644 index 00000000..00fd6846 --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/mlpbkp.htm @@ -0,0 +1,50 @@ +<html> +<head> +<title> +Netlab Reference Manual mlpbkp +</title> +</head> +<body> +<H1> mlpbkp +</H1> +<h2> +Purpose +</h2> +Backpropagate gradient of error function for 2-layer network. + +<p><h2> +Synopsis +</h2> +<PRE> +g = mlpbkp(net, x, z, deltas)</PRE> + + +<p><h2> +Description +</h2> +<CODE>g = mlpbkp(net, x, z, deltas)</CODE> takes a network data structure +<CODE>net</CODE> together with a matrix <CODE>x</CODE> of input vectors, a matrix +<CODE>z</CODE> of hidden unit activations, and a matrix <CODE>deltas</CODE> of the +gradient of the error function with respect to the values of the +output units (i.e. the summed inputs to the output units, before the +activation function is applied). The return value is the gradient +<CODE>g</CODE> of the error function with respect to the network +weights. Each row of <CODE>x</CODE> corresponds to one input vector. + +<p>This function is provided so that the common backpropagation algorithm +can be used by multi-layer perceptron network models to compute +gradients for mixture density networks as well as standard error +functions. + +<p><h2> +See Also +</h2> +<CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlpgrad.htm">mlpgrad</a></CODE>, <CODE><a href="mlpderiv.htm">mlpderiv</a></CODE>, <CODE><a href="mdngrad.htm">mdngrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file |